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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code>.RANSACRegressor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-linear-model-ransacregressor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.linear_model.RANSACRegressor</span></code></a></li>
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  <div class="section" id="sklearn-linear-model-ransacregressor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a>.RANSACRegressor<a class="headerlink" href="#sklearn-linear-model-ransacregressor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.linear_model.RANSACRegressor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.linear_model.</code><code class="sig-name descname">RANSACRegressor</code><span class="sig-paren">(</span><em class="sig-param">base_estimator=None</em>, <em class="sig-param">min_samples=None</em>, <em class="sig-param">residual_threshold=None</em>, <em class="sig-param">is_data_valid=None</em>, <em class="sig-param">is_model_valid=None</em>, <em class="sig-param">max_trials=100</em>, <em class="sig-param">max_skips=inf</em>, <em class="sig-param">stop_n_inliers=inf</em>, <em class="sig-param">stop_score=inf</em>, <em class="sig-param">stop_probability=0.99</em>, <em class="sig-param">loss='absolute_loss'</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_ransac.py#L56"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>RANSAC (RANdom SAmple Consensus) algorithm.</p>
<p>RANSAC is an iterative algorithm for the robust estimation of parameters
from a subset of inliers from the complete data set.</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#ransac-regression"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>base_estimator</strong><span class="classifier">object, optional</span></dt><dd><p>Base estimator object which implements the following methods:</p>
<blockquote>
<div><ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">fit(X,</span> <span class="pre">y)</span></code>: Fit model to given training data and target values.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">score(X,</span> <span class="pre">y)</span></code>: Returns the mean accuracy on the given test data,
which is used for the stop criterion defined by <code class="docutils literal notranslate"><span class="pre">stop_score</span></code>.
Additionally, the score is used to decide which of two equally
large consensus sets is chosen as the better one.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">predict(X)</span></code>: Returns predicted values using the linear model,
which is used to compute residual error using loss function.</p></li>
</ul>
</div></blockquote>
<p>If <code class="docutils literal notranslate"><span class="pre">base_estimator</span></code> is None, then
<code class="docutils literal notranslate"><span class="pre">base_estimator=sklearn.linear_model.LinearRegression()</span></code> is used for
target values of dtype float.</p>
<p>Note that the current implementation only supports regression
estimators.</p>
</dd>
<dt><strong>min_samples</strong><span class="classifier">int (&gt;= 1) or float ([0, 1]), optional</span></dt><dd><p>Minimum number of samples chosen randomly from original data. Treated
as an absolute number of samples for <code class="docutils literal notranslate"><span class="pre">min_samples</span> <span class="pre">&gt;=</span> <span class="pre">1</span></code>, treated as a
relative number <code class="docutils literal notranslate"><span class="pre">ceil(min_samples</span> <span class="pre">*</span> <span class="pre">X.shape[0]</span></code>) for
<code class="docutils literal notranslate"><span class="pre">min_samples</span> <span class="pre">&lt;</span> <span class="pre">1</span></code>. This is typically chosen as the minimal number of
samples necessary to estimate the given <code class="docutils literal notranslate"><span class="pre">base_estimator</span></code>. By default a
<code class="docutils literal notranslate"><span class="pre">sklearn.linear_model.LinearRegression()</span></code> estimator is assumed and
<code class="docutils literal notranslate"><span class="pre">min_samples</span></code> is chosen as <code class="docutils literal notranslate"><span class="pre">X.shape[1]</span> <span class="pre">+</span> <span class="pre">1</span></code>.</p>
</dd>
<dt><strong>residual_threshold</strong><span class="classifier">float, optional</span></dt><dd><p>Maximum residual for a data sample to be classified as an inlier.
By default the threshold is chosen as the MAD (median absolute
deviation) of the target values <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
<dt><strong>is_data_valid</strong><span class="classifier">callable, optional</span></dt><dd><p>This function is called with the randomly selected data before the
model is fitted to it: <code class="docutils literal notranslate"><span class="pre">is_data_valid(X,</span> <span class="pre">y)</span></code>. If its return value is
False the current randomly chosen sub-sample is skipped.</p>
</dd>
<dt><strong>is_model_valid</strong><span class="classifier">callable, optional</span></dt><dd><p>This function is called with the estimated model and the randomly
selected data: <code class="docutils literal notranslate"><span class="pre">is_model_valid(model,</span> <span class="pre">X,</span> <span class="pre">y)</span></code>. If its return value is
False the current randomly chosen sub-sample is skipped.
Rejecting samples with this function is computationally costlier than
with <code class="docutils literal notranslate"><span class="pre">is_data_valid</span></code>. <code class="docutils literal notranslate"><span class="pre">is_model_valid</span></code> should therefore only be used if
the estimated model is needed for making the rejection decision.</p>
</dd>
<dt><strong>max_trials</strong><span class="classifier">int, optional</span></dt><dd><p>Maximum number of iterations for random sample selection.</p>
</dd>
<dt><strong>max_skips</strong><span class="classifier">int, optional</span></dt><dd><p>Maximum number of iterations that can be skipped due to finding zero
inliers or invalid data defined by <code class="docutils literal notranslate"><span class="pre">is_data_valid</span></code> or invalid models
defined by <code class="docutils literal notranslate"><span class="pre">is_model_valid</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>stop_n_inliers</strong><span class="classifier">int, optional</span></dt><dd><p>Stop iteration if at least this number of inliers are found.</p>
</dd>
<dt><strong>stop_score</strong><span class="classifier">float, optional</span></dt><dd><p>Stop iteration if score is greater equal than this threshold.</p>
</dd>
<dt><strong>stop_probability</strong><span class="classifier">float in range [0, 1], optional</span></dt><dd><p>RANSAC iteration stops if at least one outlier-free set of the training
data is sampled in RANSAC. This requires to generate at least N
samples (iterations):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">N</span> <span class="o">&gt;=</span> <span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">probability</span><span class="p">)</span> <span class="o">/</span> <span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">e</span><span class="o">**</span><span class="n">m</span><span class="p">)</span>
</pre></div>
</div>
<p>where the probability (confidence) is typically set to high value such
as 0.99 (the default) and e is the current fraction of inliers w.r.t.
the total number of samples.</p>
</dd>
<dt><strong>loss</strong><span class="classifier">string, callable, optional, default “absolute_loss”</span></dt><dd><p>String inputs, “absolute_loss” and “squared_loss” are supported which
find the absolute loss and squared loss per sample
respectively.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">loss</span></code> is a callable, then it should be a function that takes
two arrays as inputs, the true and predicted value and returns a 1-D
array with the i-th value of the array corresponding to the loss
on <code class="docutils literal notranslate"><span class="pre">X[i]</span></code>.</p>
<p>If the loss on a sample is greater than the <code class="docutils literal notranslate"><span class="pre">residual_threshold</span></code>,
then this sample is classified as an outlier.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional, default None</span></dt><dd><p>The generator used to initialize the centers.  If int, random_state is
the seed used by the random number generator; If RandomState instance,
random_state is the random number generator; If None, the random number
generator is the RandomState instance used by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl>
<dt><strong>estimator_</strong><span class="classifier">object</span></dt><dd><p>Best fitted model (copy of the <code class="docutils literal notranslate"><span class="pre">base_estimator</span></code> object).</p>
</dd>
<dt><strong>n_trials_</strong><span class="classifier">int</span></dt><dd><p>Number of random selection trials until one of the stop criteria is
met. It is always <code class="docutils literal notranslate"><span class="pre">&lt;=</span> <span class="pre">max_trials</span></code>.</p>
</dd>
<dt><strong>inlier_mask_</strong><span class="classifier">bool array of shape [n_samples]</span></dt><dd><p>Boolean mask of inliers classified as <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
</dd>
<dt><strong>n_skips_no_inliers_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations skipped due to finding zero inliers.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>n_skips_invalid_data_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations skipped due to invalid data defined by
<code class="docutils literal notranslate"><span class="pre">is_data_valid</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>n_skips_invalid_model_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations skipped due to an invalid model defined by
<code class="docutils literal notranslate"><span class="pre">is_model_valid</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r80ce5b25cf9d-1"><span class="brackets">R80ce5b25cf9d-1</span></dt>
<dd><p><a class="reference external" href="https://en.wikipedia.org/wiki/RANSAC">https://en.wikipedia.org/wiki/RANSAC</a></p>
</dd>
<dt class="label" id="r80ce5b25cf9d-2"><span class="brackets">R80ce5b25cf9d-2</span></dt>
<dd><p><a class="reference external" href="https://www.sri.com/sites/default/files/publications/ransac-publication.pdf">https://www.sri.com/sites/default/files/publications/ransac-publication.pdf</a></p>
</dd>
<dt class="label" id="r80ce5b25cf9d-3"><span class="brackets">R80ce5b25cf9d-3</span></dt>
<dd><p><a class="reference external" href="http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf">http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf</a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">RANSACRegressor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span> <span class="o">=</span> <span class="n">RANSACRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">0.9885...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
<span class="go">array([-31.9417...])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor.fit" title="sklearn.linear_model.RANSACRegressor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Fit estimator using RANSAC algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor.get_params" title="sklearn.linear_model.RANSACRegressor.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor.predict" title="sklearn.linear_model.RANSACRegressor.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict using the estimated model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor.score" title="sklearn.linear_model.RANSACRegressor.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y)</p></td>
<td><p>Returns the score of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.linear_model.RANSACRegressor.set_params" title="sklearn.linear_model.RANSACRegressor.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">base_estimator=None</em>, <em class="sig-param">min_samples=None</em>, <em class="sig-param">residual_threshold=None</em>, <em class="sig-param">is_data_valid=None</em>, <em class="sig-param">is_model_valid=None</em>, <em class="sig-param">max_trials=100</em>, <em class="sig-param">max_skips=inf</em>, <em class="sig-param">stop_n_inliers=inf</em>, <em class="sig-param">stop_score=inf</em>, <em class="sig-param">stop_probability=0.99</em>, <em class="sig-param">loss='absolute_loss'</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_ransac.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_ransac.py#L226"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit estimator using RANSAC algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape [n_samples, n_features]</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Individual weights for each sample
raises error if sample_weight is passed and base_estimator
fit method does not support it.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><dl class="simple">
<dt>ValueError</dt><dd><p>If no valid consensus set could be found. This occurs if
<code class="docutils literal notranslate"><span class="pre">is_data_valid</span></code> and <code class="docutils literal notranslate"><span class="pre">is_model_valid</span></code> return False for all
<code class="docutils literal notranslate"><span class="pre">max_trials</span></code> randomly chosen sub-samples.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_ransac.py#L450"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict using the estimated model.</p>
<p>This is a wrapper for <code class="docutils literal notranslate"><span class="pre">estimator_.predict(X)</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">array, shape = [n_samples] or [n_samples, n_targets]</span></dt><dd><p>Returns predicted values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/linear_model/_ransac.py#L468"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the score of the prediction.</p>
<p>This is a wrapper for <code class="docutils literal notranslate"><span class="pre">estimator_.score(X,</span> <span class="pre">y)</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array or sparse matrix of shape [n_samples, n_features]</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array, shape = [n_samples] or [n_samples, n_targets]</span></dt><dd><p>Target values.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>z</strong><span class="classifier">float</span></dt><dd><p>Score of the prediction.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.linear_model.RANSACRegressor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.linear_model.RANSACRegressor.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-linear-model-ransacregressor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.linear_model.RANSACRegressor</span></code><a class="headerlink" href="#examples-using-sklearn-linear-model-ransacregressor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="In this example we see how to robustly fit a linear model to faulty data using the RANSAC algor..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_ransac_thumb.png" src="../../_images/sphx_glr_plot_ransac_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Theil-Sen Regression on a synthetic dataset."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_theilsen_thumb.png" src="../../_images/sphx_glr_plot_theilsen_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here a sine function is fit with a polynomial of order 3, for values close to zero."><div class="figure align-default" id="id6">
<img alt="../../_images/sphx_glr_plot_robust_fit_thumb.png" src="../../_images/sphx_glr_plot_robust_fit_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py"><span class="std std-ref">Robust linear estimator fitting</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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